As the cost of health care rises, many Americans seek a political solution. While some seek universal coverage through government-sponsored programs, others argue for more competition in managed care. But with health care costs approaching 17% of the GNP, and premiums rising by double digits annually, some people wonder if managed care is an experiment that has failed.
When it all began in 1930, managed care sought to harness costs through prevention and early diagnosis. (Tufts Managed Care Institute, “A Brief History of Managed Care”, Managed Health care Directory Washington, D.C.; The AMCRA Foundation, CHAA/AMCRA, and 1995-1996; Bourdon, T., Passwater, K. and Priven, M., “An Introduction to Capitation and Health Care Provider Excess Insurance”, www.casact.org/pubs/dpp/dpp97/97dpp097.pdf.)
Accomplishing this goal required information management. Even though it was paper-based, early efforts proved that linking patient symptoms to medical history and the results of past treatment not only improved quality, but also lowered cost. Yet despite their success, these plans faltered for nearly half a century, while fee-for-service medicine persisted. (Polzer, K, “Employee Health Plan Protections under ERISA”, Health Affairs, 16(5):93-102; Enthoven, A. and Singer, S., “Perspective Markets and Collective Action in Regulating Managed Care”, Health Affairs, 16(6):26-32; Wholey, D., Christianson, J., Engberg, J. and Byrce, C., “HMO Market Structure and Performance: 1985-1995”, Health Affairs, 16(6):75-84.)
Finally in 1983, with costs boiling over, the Federal Government took drastic action. Empowered by new legislation, Medicare began to pay hospitals only a portion of their customary fees based on a rate determined by one of 467 diagnosis-related groups (DRG). Shortly thereafter, private insurance released a wave of similar plans. But even with price control, which stabilized the market for a brief period, costs soon rose. (Bailit, M., “Perspective: A Purchaser's View of Health Care Market Trends”, Health Affairs, 16(6):85-88; Hellinger, F., “Any Willing Provider and Freedom of Choice Laws: An Economic Assessment”, Health Affairs 14(4):297-302; Herzlinger, R., “Market-Driven Health care: Who Wins, Who Loses in the Transformation of America's Largest Service Industry”, Perseus Books, Harper Collins Publishers, NY (1997).)
In an effort to regain control, managed care restructured itself and introduced capitation, preferred provider networks and pay-for-performance concepts. Under these new paradigms, hospitals merged into vertically-integrated giants that promised greater efficiency and lower cost through economies of scale. (Goldsmith, J., “Hospitals and Physicians: Not a Pretty Picture”, Health Affairs, 26(1):72-75; Berenson, R., Ginsburg, P. and May, J., “Hospital-Physician Relations Cooperation, or Separation?”, Health Affairs, 26(1):31-43; Goldsmith, J. and Kaufman, N., “Between a Rock and a Hard Place: Physician Markets Create New Strategic Problems for Hospitals”, COR Health care Market Strategist (November 2004).)
Unfortunately, medical information systems lagged behind these initiatives. Despite the cutting-edge use of computers in radiology and surgery, payers used computers primarily for billing and tracking procedures. Even today, considerable patient data remains locked on handwritten notes scattered across physician offices and clinics. As a result, other than the thirty-five HEDIS-mandated effectiveness-of-care measures, such as childhood immunization, hypertension control, cholesterol and beta-blockers after heart attack, colon and breast cancer screening, payers and patients primarily judge quality by price.
Generally in a free market system, competition improves products and lowers price. But in a complex service industry, like health care, prices may not reflect quality. Consumers require outcome data along with price. Absent this information, choice is governed by commercials, ads and anecdotes.
Whenever a patient faces a major medical decision, family and friends generally ask three questions: Who's your doctor? What did the doctor say? Should you get a second opinion? Under-pinning each question is the need for answers based in outcome data. Matching patients with the right doctor requires knowledge on how patients with similar conditions have responded to treatment from different providers. In addition, ethnic, family, and social needs are a concern. Collectively, this determines value.
To provide these answers, an information system must sort through electronic medical records that contain objective laboratory and clinical data, along with disability measurements, and treatments, and then correlate these to cost and outcome. Since this information is dynamic and voluminous, it must be organized and merged with existing IT systems that track codes for payment in order to draw outcome-based conclusions. Finally these conclusions must be displayed in formats that facilitate patient understanding and choice.
In the end, the question is not whether managed care failed, but rather, was information managed properly? In health care, there are three stakeholders: (a) patients seeking services at a reasonable cost; (b) payers seeking profitable risk encapsulation, and (c) providers seeking fair compensation for quality care. Each stakeholder has a unique viewpoint.
Patients are chiefly concerned with health conditions; a condition being defined as a set of symptoms and disabilities that require medical treatment over time. In most cases, with proper care, conditions improve; in others, despite superior treatment, the disease advances. However, in all cases, patients measure value by both objective and subjective means. Generally, they focus on pain relief and disability reduction. Next, they react to how that care was delivered. If medical informatics is to assist patients in selecting value, then it should display treatment results from various providers within a format that considers similar stages of disease, and co-existing problems unique to that patient.
Since value is outcome over cost, certain questions set the bench mark. Was the outcome worth the cost? Were the end results, and the improvement, worth the time and expense? During the treatment period, were the services rendered in clean comfortable facilities, by providers working together, in a manner that respected cultural differences? Such assessments consider both the science and psychology of medicine. Together they produce the final score, subjectively perceived as quality.
As outlined in Porter, M. and Teisberg, E., “Redefining Health Care: Creating Value-Based Competition on Results”, Harvard Business School Publishing, Boston Mass. (2006), these value-based outcomes should be the criteria by which providers are judged and the basis for patient-centric competition in health care.
Payers in health care are concerned with financial risk management and encapsulation of financial risk. They provide patients, employers, and the government with financial security for subscribed periods of time. The security they provide is intended to mitigate the financial consequences of disease or injury. Generally, this is achieved through financial risk management, which, to be profitable, requires accurate information on likely outcomes for specific medical conditions. Ideally, it should be achieved through ontology-driven principles that analyze clinical and laboratory data, as well as pain and disabilities before and after treatment. The current system of pooling patients under a primary ICD-9 diagnostic code and then inferring quality by the least number of CPT procedures used, disregards patient complexity, provider skills, and patient outcome.
To form value-based conclusions, patients must be grouped according to similar if not identical medical conditions. Grouping patients based on a single diagnostic (ICD-9) code will not account for complexity caused by other health cofactors such as age, obesity, renal disease, diabetes, hypertension, heart failure, or mental challenges, to name but a few. Therefore, even though patients come to providers concerned over a single chief complaint, their baseline measurement must include values that represent not only the primary disability but also all relevant health co-factors.
Next, these measurements should be standardized (i.e., normalized) and ranked according to how they influence treatment of the primary condition. Taken together, these values produce a composite number that depicts the primary disability but also relates other health cofactors that might influence outcome. We refer to this as a Patient Complexity Index (“PCIX”).
Following treatment, the change in that PCIX reflects the response to therapy. Though the amount of improvement may differ in patients, by measuring it, this system provides payers with data to evaluate disease severity and complexity, along with a patient's response to treatment. Furthermore, when this scale is coupled with cost, it permits comparison of providers by efficiency and cost.
For example, two patients with identical knee injuries might demonstrate the same restricted joint flexion, but because one of them has severe complicating health cofactors, they differ markedly from each other. Grouping these patients together under a single primary diagnostic code and expecting similar outcomes is unrealistic and prevents analysis of why their results might differ. Since current information systems are blind to the spectrum of health in any given patient, as a result they are grouped by chief complaint and their treatment is judged by the number of employed procedures and cost. This method also assumes that all providers can achieve identical results for similar problems. With no mechanism to identify best practice, payers can only achieve cost-saving by driving procedure prices lower, or delaying care.
Without multi-faceted outcome data, most providers and patients will consider pain relief as the single most-important indicator of effectiveness of treatment. Unfortunately, this is subjective and possibly temporary. Furthermore with cancer patients, it fails to answer the question as to whether the condition has been improved, or merely placated.
Since clinical measurements and laboratory data comprise a patient's unique health index, the tests which compose it should be selected and weighted in importance by national guidelines for each specific diseases and the relationship of cofactors to it. Furthermore, the system should be flexible and accommodate new diagnostic tests or treatments, as well as new understandings of co-conditions to a primary disease.
Providers employ the scientific method in nearly everything they do, except when it comes to having outsiders evaluate their work. Even if anxiety could be eliminated from the review process, there is still the need to grade physician skill and not blame disease severity and poor patient health for treatment failure. Without the ability to analyze and compare outcomes based on complete electronic medical records, failure is relegated to subjective opinions that are difficult to refute. Besides being unfair to patients, it impedes best quality practice and physician self-improvement.
Within an amorphous metric system, it's difficult to set goals and hold providers accountable. Plus, if providers have inadequate skills and enter a capitation agreement that includes patients with high complexity, they are likely to dissatisfy everyone.
On the other hand complete medical informatics allows providers to be selected and paid according to outcome data for patients with similar complexity. In this patient-centric manner, a serious impediment to capitation is removed, and the tendency to spiral sicker patients down an endless referral pattern is minimized. By recognizing the influence of relevant health co-factors and scaling payment to it, patients can be directed to integrated practice units with track records for success in comparable complexity. In such an information system, superior providers are identified and their achievements rewarded with more patient referrals.